Seismic fault image segmentation is crucial for interpreting subsurface geological structures, supporting geologists in resource exploration and structural analysis. However, current deep learning models struggle with single-architecture limitations and the distinctive characteristics of seismic faults, which are distinguished by elongated structures with uneven spatial distributions. To address these challenges, we propose TopSUMseg, a novel Topology-Aware Swin Transformer-Mamba framework for 3D seismic fault image segmentation. Our framework combines Swin Transformer’s local feature extraction with Mamba’s efficient sequence modeling, and boosts 3D spatial modeling in Mamba with a newly designed Global-Local Attention module (GLA). Additionally, we design a Topology-Aware Structural Constraint (TASC) to align predictions with ground-truth structures in the feature space, promoting the modeling of complex fault geometries. Experiments on Thebe, the largest public seismic dataset, demonstrate that TopSUMseg achieves state-of-the-art performance with OIS and ODS scores of 0.879 and 0.875, respectively. Trained entirely from scratch, TopSUMseg nonetheless achieves superior performance compared to extensively pre-trained counterparts. In addition, TopSUMseg maintains a significantly lower parameter count while achieving a favorable trade-off between segmentation performance and time complexity, making it a practical and generalizable solution for real-world seismic fault interpretation.

Recent advances in deep learning have greatly improved the segmentation of mitochondria from Electron Microscopy (EM) images. However, suffering from variations in mitochondrial morphology, imaging conditions, and image noise, existing methods still exhibit high uncertainty in their predictions. Moreover, in view of our findings, predictions with high levels of uncertainty are often accompanied by inaccuracies such as ambiguous boundaries and amount of false positive segments. To deal with the above problems, we propose a novel approach for mitochondria segmentation in 3D EM images that leverages evidential uncertainty estimation, which for the first time integrates evidential uncertainty to enhance the performance of segmentation. To be more specific, our proposed method not only provides accurate segmentation results, but also estimates associated uncertainty. Then, the estimated uncertainty is used to help improve the segmentation performance by an uncertainty rectification module, which leverages uncertainty maps and multi-scale information to refine the segmentation. Extensive experiments conducted on four challenging benchmarks demonstrate the superiority of our proposed method over existing approaches.

The Amodal Instance Segmentation (AIS) task aims to infer the complete mask of occluded instance. Under many circumstances, existing methods treat occluded objects as unoccluded ones, and vice versa, leading to inaccurate predictions. This is because existing AIS methods do not explicitly utilize the occlusion rates of each object as supervision. However, occlusion information is critical for the methods to recognize whether the target objects are occluded. Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance. In this paper, a simple yet effective Occlusion-aware transformer-based model, OAFormer, is proposed for accurate amodal instance segmentation. The goal of OAFormer is to learn the occlusion discriminative features. Novel components are proposed to enable OAFormer to be occlusion distinguishable. We conduct extensive experiments on two challenging AIS datasets to evaluate the effectiveness of our method. OAFormer outperforms state-of-the-art methods by large margins.

MOTIVATION: Cell membrane segmentation in electron microscopy (EM) images is a crucial step in EM image processing. However, while popular approaches have achieved performance comparable to that of humans on low-resolution EM datasets, they have shown limited success when applied to high-resolution EM datasets. The human visual system, on the other hand, displays consistently excellent performance on both low and high resolutions. To better understand this limitation, we conducted eye movement and perceptual consistency experiments. Our data showed that human observers are more sensitive to the structure of the membrane while tolerating misalignment, contrary to commonly used evaluation criteria. Additionally, our results indicated that the human visual system processes images in both global-local and coarse-to-fine manners. RESULTS: Based on these observations, we propose a computational framework for membrane segmentation that incorporates these characteristics of human perception. This framework includes a novel evaluation metric, the perceptual Hausdorff distance (PHD), and an end-to-end network called the PHD-guided segmentation network (PS-Net) that is trained using adaptively tuned PHD loss functions and a multiscale architecture. Our subjective experiments showed that the PHD metric is more consistent with human perception than other criteria, and our proposed PS-Net outperformed state-of-the-art methods on both low- and high-resolution EM image datasets as well as other natural image datasets. AVAILABILITY AND IMPLEMENTATION: The code and dataset can be found at https://github.com/EmmaSRH/PS-Net.

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).